{"id":"https://openalex.org/W2946206667","doi":"https://doi.org/10.1145/3316781.3317908","title":"Sensitivity based Error Resilient Techniques for Energy Efficient Deep Neural Network Accelerators","display_name":"Sensitivity based Error Resilient Techniques for Energy Efficient Deep Neural Network Accelerators","publication_year":2019,"publication_date":"2019-05-23","ids":{"openalex":"https://openalex.org/W2946206667","doi":"https://doi.org/10.1145/3316781.3317908","mag":"2946206667"},"language":"en","primary_location":{"id":"doi:10.1145/3316781.3317908","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Design Automation Conference 2019","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045814929","display_name":"Wonseok Choi","orcid":"https://orcid.org/0000-0002-7566-6945"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wonseok Choi","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091058036","display_name":"Dongyeob Shin","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongyeob Shin","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101839916","display_name":"Jongsun Park","orcid":"https://orcid.org/0000-0003-3251-0024"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongsun Park","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085567454","display_name":"Swaroop Ghosh","orcid":"https://orcid.org/0000-0001-8753-490X"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Swaroop Ghosh","raw_affiliation_strings":["Pennsylvania State University, University Park, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10472","display_name":"Semiconductor materials and devices","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.7491012215614319},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6962367296218872},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5692770481109619},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5471358895301819},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5052440762519836},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4953140318393707},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.48516732454299927},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4629431962966919},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.460679829120636},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.4493723511695862},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4288618862628937},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.29606562852859497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2004438042640686},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15989789366722107},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11814674735069275},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08661195635795593},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06394308805465698}],"concepts":[{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.7491012215614319},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6962367296218872},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5692770481109619},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5471358895301819},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5052440762519836},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4953140318393707},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.48516732454299927},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4629431962966919},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.460679829120636},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.4493723511695862},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4288618862628937},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.29606562852859497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2004438042640686},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15989789366722107},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11814674735069275},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08661195635795593},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06394308805465698},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3316781.3317908","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Design Automation Conference 2019","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2043318181","https://openalex.org/W2048266589","https://openalex.org/W2193413348","https://openalex.org/W2271840356","https://openalex.org/W2289252105","https://openalex.org/W2606722458","https://openalex.org/W2618530766","https://openalex.org/W2774688396","https://openalex.org/W2953106684","https://openalex.org/W2953212265","https://openalex.org/W2963122961","https://openalex.org/W2963287528","https://openalex.org/W2963640628","https://openalex.org/W2964299589","https://openalex.org/W3024621361","https://openalex.org/W3147600416","https://openalex.org/W4238485759"],"related_works":["https://openalex.org/W4378770497","https://openalex.org/W2049584446","https://openalex.org/W2079781215","https://openalex.org/W4385571583","https://openalex.org/W2064404759","https://openalex.org/W4389519396","https://openalex.org/W4308245303","https://openalex.org/W2014033564","https://openalex.org/W2910573937","https://openalex.org/W2113438243"],"abstract_inverted_index":{"With":[0,80],"inherent":[1],"algorithmic":[2],"error":[3,30,43,77,83,154],"resilience":[4,44],"of":[5,42,61,85,161],"deep":[6],"neural":[7],"networks":[8],"(DNNs),":[9],"supply":[10],"voltage":[11,36],"scaling":[12,37],"could":[13],"be":[14,100],"a":[15],"promising":[16],"technique":[17,156],"for":[18],"energy":[19,140],"efficient":[20],"DNN":[21,49,104],"accelerator":[22],"design.":[23],"In":[24],"this":[25],"paper,":[26],"we":[27],"propose":[28],"novel":[29],"resilient":[31],"techniques":[32],"to":[33,48,55,102,116,129,151],"enable":[34],"aggressive":[35],"by":[38],"exploiting":[39],"different":[40],"amount":[41],"(sensitivity)":[45],"with":[46,110,123,145,157],"respect":[47],"layers,":[50],"filters,":[51],"and":[52],"channels.":[53],"First,":[54],"rapidly":[56],"evaluate":[57],"filter/channel-level":[58],"weight":[59,73],"sensitivities":[60],"large":[62],"scale":[63],"DNNs,":[64],"first-order":[65],"Taylor":[66],"expansion":[67],"is":[68],"used,":[69],"which":[70],"accurately":[71],"approximates":[72],"sensitivity":[74,94],"from":[75],"actual":[76],"injection":[78],"simulation.":[79],"measured":[81],"timing":[82,137,153],"probability":[84],"each":[86],"multiply-accumulate":[87],"(MAC)":[88],"units":[89],"considering":[90],"process":[91],"variations,":[92],"the":[93,108,158],"variation":[95],"among":[96],"filter":[97],"weights":[98,113,126],"can":[99],"leveraged":[101],"design":[103],"accelerator,":[105],"such":[106],"that":[107],"computations":[109],"more":[111,117],"sensitive":[112,125],"are":[114,127],"assigned":[115,128],"robust":[118,131],"MAC":[119,132],"units,":[120],"while":[121],"those":[122],"less":[124,130],"units.":[133],"Based":[134],"on":[135],"post-synthesis":[136],"simulations,":[138],"51%":[139],"savings":[141],"has":[142],"been":[143],"achieved":[144],"CIFAR-10":[146],"dataset":[147],"using":[148],"VGG-9":[149],"compared":[150],"state-of-the-art":[152],"recovery":[155],"same":[159],"constraint":[160],"3%":[162],"accuracy":[163],"loss.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
